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Multidimensional Permutation Entropy for Constrained Motif Discovery

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Intelligent Information and Database Systems (ACIIDS 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11431))

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Abstract

Constrained motif discovery was proposed as an unsupervised method for efficiently discovering interesting recurrent patterns in time-series. The de-facto standard way to calculate the required constraint on motif occurrence locations is change point discovery. This paper proposes the use of time-series complexity for finding the constraint and shows that the proposed approach can achieve higher accuracy in localizing motif occurrences and approximately the same accuracy for discovering different motifs at three times the speed of change point discovery. Moreover, the paper proposes a new extension of the permutation entropy for estimating time-series complexity to multi-dimensional time-series and shows that the proposed extension outperforms the state-of-the-art multi-dimensional permutation entropy approach both in speed and usability as a motif discovery constraint.

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Authors and Affiliations

  1. Assiut University, Asyut, Egypt

    Yomna Rayan, Yasser Mohammad & Samia A. Ali

  2. AIST, Tokyo, Japan

    Yasser Mohammad

Authors
  1. Yomna Rayan
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  2. Yasser Mohammad
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  3. Samia A. Ali
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Corresponding authors

Correspondence to Yomna Rayan or Yasser Mohammad .

Editor information

Editors and Affiliations

  1. Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Ngoc Thanh Nguyen

  2. Bina Nusantara University, Jakarta, Indonesia

    Ford Lumban Gaol

  3. National University of Kaohsiung, Kaohsiung, Taiwan

    Tzung-Pei Hong

  4. Wrocław University of Science and Technology, Wrocław, Poland

    Bogdan Trawiński

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Rayan, Y., Mohammad, Y., Ali, S.A. (2019). Multidimensional Permutation Entropy for Constrained Motif Discovery. In: Nguyen, N., Gaol, F., Hong, TP., Trawiński, B. (eds) Intelligent Information and Database Systems. ACIIDS 2019. Lecture Notes in Computer Science(), vol 11431. Springer, Cham. https://doi.org/10.1007/978-3-030-14799-0_20

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  • DOI: https://doi.org/10.1007/978-3-030-14799-0_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-14798-3

  • Online ISBN: 978-3-030-14799-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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